Something uncomfortable is happening in cybersecurity, and most business leaders are not yet reckoning with it. The same AI capabilities that are transforming productivity, customer service, and data analysis are being weaponised by threat actors at a scale and sophistication that renders traditional security architecture dangerously inadequate.
I have been tracking this trend closely — both as an investor through NexaTech Ventures, where cybersecurity AI is an active investment thesis, and as someone who has spent twenty years in technology environments where security was always part of the operational conversation. What I see in 2026 is an arms race that the defensive side is currently losing.
How AI Has Changed the Threat Landscape
The shift is not subtle. AI has transformed the economics of cyberattacks in three fundamental ways.
First, AI-generated phishing. Large language models can now produce phishing emails that are grammatically flawless, contextually appropriate, and personalised to the target using publicly available data. The tell-tale signs that trained employees to spot phishing — awkward phrasing, generic greetings, obvious urgency — have been eliminated. Phishing success rates against organisations that have invested heavily in security awareness training have increased markedly since 2024. The training has not got worse; the attacks have got dramatically better.
Second, AI-powered vulnerability discovery. Automated systems can now scan codebases, network configurations, and application architectures for vulnerabilities at a speed that human security researchers cannot match. This capability exists on both sides — defensive scanning tools and offensive exploitation tools — but the asymmetry favours attackers. Defenders must find and patch every vulnerability. Attackers need to find one.
Third, deepfake-enabled social engineering. Voice cloning, video synthesis, and real-time deepfake generation have reached a quality threshold where they are being used in targeted attacks against executives and finance teams. Several high-profile cases in 2025 involved deepfake video calls where attackers impersonated senior executives to authorise wire transfers. The amounts involved were in the tens of millions.
Why Traditional Defences Are Failing
The cybersecurity industry has spent two decades building defences based on pattern recognition: signature-based antivirus, rule-based firewalls, known-threat databases. These approaches work against known attack vectors. They fail against AI-generated attacks that are novel by design.
The fundamental problem is that AI-powered attacks are polymorphic at a speed that static defences cannot match. A phishing email generated by an LLM is unique every time it is sent. A vulnerability exploit crafted by an AI system can mutate its approach based on the defensive responses it encounters. Traditional security tools that rely on matching incoming threats against databases of known signatures are fighting the last war.
This does not mean traditional security is worthless — basic cyber hygiene, patching discipline, access control, and network segmentation remain essential. But they are necessary and no longer sufficient. The organisations that understand this distinction are the ones that will survive the current threat environment.
The AI Defence Opportunity
The defensive response must be AI-native. This means security systems that use machine learning to detect anomalous behaviour in real time, rather than matching against static threat databases. The most promising approaches fall into several categories.
Behavioural analytics — systems that learn the normal patterns of user behaviour, network traffic, and application activity within an organisation and flag deviations. These systems do not need to know what a specific attack looks like; they need to recognise that something abnormal is happening. The best implementations I have seen through NexaTech’s deal flow combine endpoint telemetry, network flow data, and user behaviour analytics into a unified detection platform.
AI-powered email security — systems that go beyond content analysis to evaluate sender behaviour patterns, communication context, and linguistic anomalies. The companies building this capability are essentially running defensive LLMs against offensive LLMs, and the arms race dynamics are intense.
Automated incident response — systems that can detect, contain, and remediate certain categories of attack without human intervention. The speed of AI-powered attacks means that the time between initial compromise and data exfiltration has compressed from days to hours, and in some cases minutes. Human-only incident response cannot operate at this pace.
What European Businesses Must Do Now
For business leaders reading this, the practical implications are these.
Audit your current security architecture against AI-powered threats specifically. Ask your security team whether your defences can detect novel, AI-generated phishing at scale. If the honest answer is no, you have a gap that needs addressing urgently.
Invest in AI-native security tooling. The market is maturing rapidly, and the solutions available in 2026 are substantially more capable than those of even two years ago. The cost of deployment is falling while the cost of a breach is rising. The economic case is straightforward.
Train your people differently. Traditional security awareness training focused on recognising poorly written phishing emails is obsolete. Training needs to focus on verification procedures — how to confirm the identity of someone requesting a wire transfer, how to validate an unexpected communication from a senior executive, how to report something that feels wrong even if it looks right.
And for the investors among you: the AI cybersecurity sector is producing genuinely differentiated technology companies. The market is large, the need is urgent, and the companies that establish themselves as leaders in AI-native defence will build substantial and defensible businesses.
Scott Dylan is the Founder of NexaTech Ventures. He writes on AI, cybersecurity, and technology investment. (Disclaimer: Scott Dylan is not a shareholder of Nexatech Ventures)
